Speaker recognition based on short utterance compensation method of generative adversarial networks

摘要

On the basis of gaussian mixture model-universal background model (GMM-UBM) in the speaker recognition system, the paper proposes a short utterance sample compensation method based on the generative adversarial network (GAN) to solve the problem of the inadequate corpus data caused by short utterance, which has led to a serious reduction of recognition rate. The presented method compensates the short utterance samples into the speech samples with sufficient speaker identity information by completing the antagonistic training of generator network and discriminator network. In order to avoid the model crash and gradient instability in the process of GAN training, this paper adopts the condition information in the conditional GAN to guide the compensation process of the generator network, and proposes the generator compensation performance measurement training task and the feature tag training task of the discriminator to stabilize training process. Finally, the proposed short utterance compensation method is evaluated on the speaker recognition system based on GMM-UBM. The experimental results indicate that the presented method can effectively reduce the equal error rate of the speaker recognition system in short utterance environment.